We present an efficient criterion for doing fast estimations of the critical temperature of hydrogen based superconductors. We start by expanding the applicability of 3D descriptors of electron localization to superconducting states within the framework of superconducting DFT. We first apply this descriptor to a model system, the hydrogen chain, which allows to prove two main concepts: i) that the electron localization changes very little when the transition from the normal to the superconducting state takes place, i.e. that it can be described at the DFT level from the normal state; and ii) that the formation of molecules can be characterized within this theoretical framework, enabling to quickly filter out systems with marked molecular character and hence with low potential to be good superconductors. These two ideas, are then exploited in real binary and ternary systems, showing i) that the bonding type can be characterized automatically; and ii) that this provides a new index which enables to feed machine learning algorithms for a better prediction of critical temperatures. Overall, this sets a grounded theoretical scenario for an automatic and efficient high-throughput screening of potential hydrogen based superconductors.