Crystallography is a powerful descriptor of the atomic structure of solid state matter and can be applied to analyse the phenomena present in functional materials. Especially for diffusion — one of the main processes found in electrochemical energy storage materials — crystallography can describe and evaluate the elementary steps for the hopping of an atom or ion from one crystallographic site to another. By translating this knowledge into parameters and search for similar numbers in other materials, interesting compounds for future energy storage materials can be identified. Large crystal structure databases like the ICSD, CSD, and PCD have accumulated millions of measured crystal structures and thus represent valuable sources for future data mining and big-data approaches. In this work we want to present on the one hand side crystallographic approaches based on geometric and crystal-chemical descriptors that can be easily applied to very large databases and show, on the other hand side, other methodologies based on ab initio and electronic modelling which can simulate the structures features more realistically, incorporating also dynamic processes. Their theoretic background, applicability, and examples are presented.