Using these mature, well-tested CSP methods alongside modern machine learning approaches, we have demonstrated the ability of CSP to create very large, diverse datasets of hypothetical crystal structures, and the utility of this information in both understanding broad trends in organic crystal structures and in training more advanced energetic models for refinement and transferability. It is our hope that the variety and quantity of CSP data presented here, alongside our demonstrations of possible applications, enables the greater organic solid-state computational community to develop even more sophisticated models and techniques in pursuit of truly predictive, computational data-driven discovery of novel materials.