The Computational Literary Studies (CLS) field will be facing many developments, with newly emerging technologies and evolving research infrastructures rapidly shaping its future. While the field has already made important contributions in applying computational methods to literary research, challenges such as fragmented tools, lack of sustainable data pathways, and limited interdisciplinary integration persist. This roadmap was created in the context of the EU-funded Computational Literary Studies Infrastructure (CLS INFRA) project. Based on conversations with project members and existing research on the future of the CLS field, the roadmap identifies key areas for future development, such as shared technical infrastructures, reproducible and exemplary workflows, and closer collaboration between computational and traditional literary studies scholars. The integration of Linked Open Data (LOD) and Large Language Models (LLMs) offers new opportunities for more advanced, interconnected and better annotated CLS research, however, their implementation requires careful consideration as pertains to accessibility, transparency, and long-term sustainability. Additionally, fostering a global community of practice and embedding CLS methodologies into broader literary scholarship will be crucial for securing its place in the future of the humanities. By addressing these challenges, CLS can move toward a more cohesive and sustainable research ecosystem.