Relative binding free energy (RBFE) calculations have shown over the years to be an incredibly powerful tool in supporting ligand optimization efforts. Despite many successes, the use of RBFEs can often be limited by problems with applying these approaches, in particular automating calculation setup procedures. Atom mapping algorithms are an essential component in setting up hybrid topology RBFE calculation campaigns, particularly when applied at large scales. Traditional algorithms typically use a Maximum Common Substructure approach, which has limitations when dealing with chemical properties related to geometry, and can lead to suboptimal solutions. To overcome these limitations, we have developed kartograf, a Python package for the planning of free energy networks which features a geometric-graph based algorithm. The algorithm uses primarily the 3D coordinates of atoms to find a mapping between two ligands. Usually in free energy approaches, the ligand conformations are derived from docking or other previous modeling approaches giving the coordinates a certain importance. By considering the spatial relationships between atoms related to the ligand poses, our algorithm can bypass the computationally complex subgraph matching approach of MCS and reduce the problem to a much simpler sparse graph matching problem. Additionally, this algorithm circumvents typical mapping problems induced by molecule symmetry and stereoisomerism, making it a more robust approach for atom mapping from a geometric perspective. We validate katograf against current gold standard methods, namely Lomap, applying it to the calculation of relative hybrid free energies for a diverse set of small molecules. Our results demonstrate that kartograf offers an interesting alternative approach. The code for kartograf is freely available on Github (https://github.com/OpenFreeEnergy/kartograf). Kartograf has been developed for the OpenFE ecosystem, but can also be used as a standalone python package.