Learning character embeddings with GenSim
I believe that you misunderstood the word2vec concept. Basically for words, the feature vector for a word is learnt from the surrounding words. 

You shall know a word by the company it keeps- Firth.J.R

In your case characters have been used, so the feature vector for each character depends upon the adjacent characters present. your example might work, if you have the following training sentences.


ABCDEFGHI 
abcdefghi 
AbcDEFghi 
aBcdEfgHI 
abcDEFgHi 
ABcdEFGHi


With these training sentences, the characters 'A','a','B','b' will preserve capital features and english alphabet order feature. But whereas, when trained with wikipedia sentences, the characters will preserve the probability of being present in a meaningful word. For instance, the closest letter to 'C' would be 'a','o','e' but hardly 'x' or 'd' because there would be words like 'covenant','country','cat' as no common words would be 'cx..'