From a modeling point of view, the approach we present constitutes a precious tool to understand how to build rigorous coarse-grained models preserving the global structural and dynamic features of these assemblies, or how to chose critical collective variables to explore and enhance the sampling of determined transitions in the system in an accurate way during biased molecular simulations (e.g., metadynamics). From a chemistry and materials science fundamental point of view, we show a unique data-driven approach to characterize, compare and classify self-assembled supramolecular polymers based on their structural/dynamic diversity and on their defects, setting the stage for a systematic investigation of the interplay between monomer chemistry, solvent environment, and the final properties of the supramolecular assembly.