Scanning multiple huge files in Python
Some smaller adjustments

By just quickly glancing on the code I would suggest the following actions:


Move the opening of the output file to where you open the inSortedKey file, and let it be open until finished processing the files. Opening and closing files is rather expensive. 
Instead of splitting the line multiple times, do it once in the start of searchWindow() and use the different elements when you need them. Possibly skip the intermediate variables all together
Simplify the logic related to the isHere if statements, as you do the same in both cases only that in the second case click will always be 0. Thusly eliminate the duplication of code here
Consider using another level of with open(...) and for another_line instead of the next((line for line ... )), and do early return in that loop to skip lines as soon as possible


A little style advise to close off: Remember that the Python style guide indicates to use snake_case for both variables and function names. :-)

Multiple reads of dataset

For each line in inSortedKey file you read all lines from each of the files in the dataset. That is a lot of extra reading of files!!! Lets say that you have \$n\$ lines in inSortedKeys and \$m\$ files in the dataset, where each file has approximately \$p\$ lines. Then your code does \$O(n\cdot m\cdot p)\$ operations within searchWindows. 

With your numbers being: \$n=62.000.000\$, \$m=10\$, and \$p=16.000.000\$. 

If you had been able to preread the inSortedKey and extract the keys into a memory table, and then do a single read on each of the other dataset files matching each line against this precomputed key list this number would drop to \$O(n + m \cdot p)\$. That would really speed up your operations, but it does depend on the size of inSortedKey. 

Even if you are not able to load all of inSortedKey you would be much better of reading as large a chunk as you are comfortable with into memory, before doing a full read on the dataset. Say you read 100000 lines of inSortedKeys into memory, you would look at \$O(\frac{n}{100000} \cdot m \cdot p)\$ operations doing a slightly heavier operation each time. It would still be a massive improvement.

Consider switching to databases

If you're actually talking about cross-referencing 62 million keys against 160 millions lines in the dataset files, you should seriously consider using databases. 

Either permanently, or temporarily. You would most likely get a performance gain if you imported the entire set into a database, did your operations, and wrote the output rather than doing this with plain file operations. Python has excellent support for sqlite or pandas, in addition to multiple other modules and databases, which might help you.

See 200_success' answer for a somewhat related discussion on massive file sizes.