Non-biological foldamers are a promising class of macromolecules that share similarities to classical biopolymers such as proteins and nucleic acids. Currently, designing novel foldamers is a non-trivial process, often involving many iterations of trial synthesis and characterization until folded structures are observed. In this work, we aim to tackle these foldamer design challenges using computational modeling techniques. We developed CG PyRosetta, an extension to the popular protein folding python package, PyRosetta, which introduces coarse-grained (CG) residues into PyRosetta, enabling the folding of toy CG foldamer models. Through systematic variation of CG parameters in these models, we can investigate various folding hypotheses and generate folding principles at the CG scale to inform the design process of new foldamer chemistries. We demonstrate CG PyRosetta’s ability to identify minimum energy structures with a diverse structural search over a range of simple models and two hypothesis-driven parameter scans investigating the effects of side-chain size and internal backbone angle on secondary structure. We are able to identify several types of secondary structures from single- and double-helices to sheet-like and knot-like structures. We show how side-chain size and backbone bond angle both play an important role in the structure and energetics of these toy models. Optimal side-chain sizes promote favorable packing of side-chains, while specific backbone bond-angles influence the specific helix type found in folded structures.