Defects control important properties in various types of materials, and are also key in soft selfassembled materials such as supramolecular polymers. Here we employed a data driven approach to extract directly from well-sampled coarse-grained molecular dynamics (CG-MD) simulations information on the molecular motifs and defects in variants of BTA supramolecular polymers, that we use as a demonstration and benchmark. We employed a general and high-dimensional descriptor of molecular environments (Smooth Overlap of Atomic Positions, SOAP vectors) and the Probabilistic Analysis of Molecular Motifs (PAMM) to automatically recognize and classify the prevalent structural states, developing a protocol that could be applied with minimal modifications to other classes of supramolecular materials. For example, in this case the generality of the approach allowed us to perform a transferable analysis and to compare directly between defects in different variants of these supramolecular polymers.